Claude Opus 4.7 vs GPT-5.4 Nano

Across the 12 tests in our suite, Claude Opus 4.7 wins 4, GPT-5.4 Nano wins 2, and they tie on 6. Neither model has been assigned an overall average score in our data, so this test-by-test breakdown is the primary basis for comparison.

Where Opus 4.7 leads:

• Tool calling (5 vs 4): Opus 4.7 ties for 1st among 55 models tested; Nano ranks 19th of 55. This is a material gap for agentic workflows — tool calling covers function selection, argument accuracy, and multi-step sequencing. A one-point difference here can mean the difference between a reliable agent and one that misfires under complex conditions.

• Agentic planning (5 vs 4): Opus 4.7 ties for 1st among 55 models; Nano ranks 17th of 55. Goal decomposition and failure recovery at scale favor Opus 4.7 clearly.

• Faithfulness (5 vs 4): Opus 4.7 ties for 1st among 56 models; Nano ranks 35th of 56. For RAG pipelines, document summarization, or any task where sticking to source material matters, this gap is significant — hallucination risk is meaningfully higher with Nano on this dimension.

• Creative problem solving (5 vs 4): Opus 4.7 ties for 1st among 55 models; Nano ranks 10th of 55. This covers non-obvious, specific, feasible ideation — relevant for research assistance, strategic brainstorming, and product design tasks.

Where Nano leads:

• Structured output (5 vs 4): Nano ties for 1st among 55 models; Opus 4.7 ranks 26th of 55. For JSON schema compliance and format adherence — critical in data pipelines and API integrations — Nano actually has the edge. This is worth noting for developers building structured extraction or schema-constrained generation systems.

• Multilingual (5 vs 4): Nano ties for 1st among 56 models; Opus 4.7 ranks 36th of 56. For non-English output quality, Nano delivers at the top of the field while Opus 4.7 sits in the bottom third.

Ties (6 of 12 tests):

Both models score identically on strategic analysis (5/5), constrained rewriting (4/4), classification (3/3), long context (5/5), safety calibration (3/3), and persona consistency (5/5). The safety calibration tie at 3/3 is worth flagging — both models sit at rank 10 of 56, meaning most models in our testing score lower on this dimension, but neither Opus 4.7 nor Nano is at the top of the field.

External benchmark — math (Epoch AI): GPT-5.4 Nano scores 87.8% on AIME 2025, ranking 8th of 23 models measured by Epoch AI on this competition-math benchmark. This places it above the median (83.9%) for models tested on that task. Claude Opus 4.7 has no AIME 2025 score in the data available to us, so a direct comparison cannot be made. The Nano AIME result is meaningful independent signal for math-heavy use cases.

The pricing gap here is not a nuance — it is a 20x multiplier. Claude Opus 4.7 runs $5 per million input tokens and $25 per million output tokens. GPT-5.4 Nano runs $0.20 per million input tokens and $1.25 per million output tokens.

At 1 million output tokens per month, Opus 4.7 costs $25 versus Nano's $1.25 — a $23.75 difference that is easy to absorb. At 10 million output tokens, that gap becomes $250 vs $12.50, a $237.50 delta per month. At 100 million output tokens — realistic for a production app with active users — Opus 4.7 runs $2,500 versus Nano's $125, a $2,375 monthly difference.

Who should care: developers building pipelines that generate large volumes of output (summarization, document drafting, high-throughput classification) need to model this gap carefully. Opus 4.7 makes economic sense when the task genuinely requires its stronger agentic planning, tool calling, or faithfulness — scenarios where errors are costly or where each output replaces significant human effort. For bulk data processing, customer-facing chat at scale, or any workflow where Nano's scores are sufficient, the cost argument for Nano is hard to beat. Nano's context window is also 400,000 tokens versus Opus 4.7's 1,000,000 — a meaningful difference only for tasks processing very long documents.

Choose Claude Opus 4.7 if:

• You are building multi-step agentic systems where tool calling reliability and planning depth are critical — Opus 4.7 scores 5/5 on both, ranking in the top tier of 55 models tested.
• Faithfulness to source material is non-negotiable: RAG pipelines, legal summarization, or document-grounded QA where hallucinations carry real cost.
• Creative problem solving quality justifies the premium — Opus 4.7 ties for 1st of 55 models on this dimension.
• Your context window requirements exceed 400,000 tokens (Opus 4.7 supports 1,000,000 tokens vs Nano's 400,000).
• Volume is low enough that the $25/M output token cost is manageable relative to the task value.

Choose GPT-5.4 Nano if:

• You need reliable structured output at scale — Nano ties for 1st of 55 models on JSON schema compliance, edging out Opus 4.7.
• Your application is multilingual: Nano ties for 1st of 56 models tested, while Opus 4.7 ranks 36th.
• Volume is high (10M+ output tokens/month) and the benchmark gaps don't justify a 20x cost increase — Nano's $1.25/M output token price is among the lowest in its performance class.
• You need math reasoning capability: Nano's 87.8% on AIME 2025 (Epoch AI, rank 8 of 23) provides third-party validation for quantitative tasks.
• File input support matters for your workflow — the payload explicitly lists file inputs among Nano's supported modalities.